[Monitoring the Paraguayan epidemiological dengue surveillance system (2009-2011) using Benford's law]

Epidemiological anomaly detection in Philippine public health surveillance data through Newcomb-Benford analysis

BACKGROUND

Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health.

METHODS

Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations.

RESULTS

Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations.

CONCLUSIONS

The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance.

Socioepidemiological macro-determinants associated with the cumulative incidence of bacterial meningitis: A focus on the African Meningitis Belt

Background

Bacterial meningitis (BM) is a public health challenge as it is associated with high lethality and neurological sequelae. Worldwide, most cases are registered in the African Meningitis Belt (AMB). The role of particular socioepidemiological features is essential for understanding disease dynamics and optimizing policy-making.

Objective

To identify socioepidemiological macro-determinants that contribute to explaining the differences in BM incidence between AMB and the rest of Africa.

Methods

Country-level ecologic study based on the cumulative incidence estimates of the Global Burden of Disease study and reports of the MenAfriNet Consortium. Data about relevant socioepidemiological features were extracted from international sources. Multivariate regression models were implemented to define variables associated with the classification of African countries within the AMB and the incidence of BM worldwide.

Results

Cumulative incidences at the AMB sub-regions were 111.93 (west), 87.23 (central), 65.10 (east), and 42.47 (north) per 100,000 population. A pattern of common origin with continuous exposition and seasonality of cases was observed. Socio-epidemiological determinants contributing to differentiating the AMB from the rest of Africa were household occupancy (OR 3.17 CI 95% 1.09-9.22, p = 0.034) and malaria incidence (OR 1.01 CI 95% 1.00-1.02, p = 0.016). BM cumulative incidence worldwide was additionally associated with temperature and gross national income per capita.

Conclusion

Socioeconomic and climate conditions are macro-determinants associated with BM cumulative incidence. Multilevel designs are required to confirm these findings.

Using the Newcomb-Benford law to study the association between a country's COVID-19 reporting accuracy and its development

The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy. We run an OLS regression of an index constructed from developmental indicators (democracy level, gross domestic product per capita, healthcare expenditures, and universal healthcare coverage) on goodness-of-fit measures to the NBL. We find that countries with higher values of the developmental index are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests and for a sub-sample of countries with regional data. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.

Benford’s Law for Telemetry Data of Wildlife

Benford’s law (BL) specifies the expected digit distributions of data in social sciences, such as demographic or financial data. We focused on the first-digit distribution and hypothesized that it would apply to data on locations of animals freely moving in a natural habitat. We believe that animal movement in natural habitats may differ with respect to BL from movement in more restricted areas (e.g., game preserve). To verify the BL-hypothesis for natural habitats, during 2015–2018, we collected telemetry data of twenty individuals of wild red deer from an alpine region of Austria. For each animal, we recorded the distances between successive position records. Collecting these data for each animal in weekly logbooks resulted in 1132 samples of size 65 on average. The weekly logbook data displayed a BL-like distribution of the leading digits. However, the data did not follow BL perfectly; for 9% (99) of the 1132 weekly logbooks, the chi-square test refuted the BL-hypothesis. A Monte Carlo simulation confirmed that this deviation from BL could not be explained by spurious tests, where a deviation from BL occurred by chance.

Report From Bolsonaro's Brazil: The Consequences of Ignoring Science

Currently, the fast spread of COVID-19 is the cause of a sanitary emergency in Brazil. This situation is largely due to President Bolsonaro's denial and the uncoordinated actions between the federal and local governments. In addition, the Brazilian government has reported that it would change its method of sharing information about the pandemic. On June 6, 2020, the presentation of accumulated cases and deaths was stopped, and the Supreme Court of Brazil determined that the federal government should continue to consolidate and disseminate the accumulated figures of cases and deaths. However, doubt about the transparency of the data remained. We used data reported by the government from Situation Reports 38-209 of the World Health Organization to assess the Benford's law fulfillment as an indicator of data quality. This rapid evaluation of data quality during the ongoing COVID-19 pandemic in Brazil suggests that the Brazilian public health surveillance system had an acceptable performance at the beginning of the epidemic. Since the end of June, the quality of cumulative death data began to decrease and remains in that condition as of August 2020. A similar situation has existed since August, with the data of accumulated new cases.

On the authenticity of COVID-19 case figures

In this article, we study the applicability of Benford’s law and Zipf’s law to national COVID-19 case figures with the aim of establishing guidelines upon which methods of fraud detection in epidemiology, based on formal statistical analysis, can be developed. Moreover, these approaches may also be used in evaluating the performance of public health surveillance systems. We provide theoretical arguments for why the empirical laws should hold in the early stages of an epidemic, along with preliminary empirical evidence in support of these claims. Based on data published by the World Health Organization and various national governments, we find empirical evidence that suggests that both Benford’s law and Zipf’s law largely hold across countries, and deviations can be readily explained. To the best of our knowledge, this paper is among the first to present a practical application of Zipf’s law to fraud detection.

Benchmarking of public health surveillance of COVID-19 in Colombia: First semester

Introduction: Public health surveillance together with good sanitary decisions is essential for the proper management of the SARS-CoV-2 pandemic. Objective: To compare the performance of Colombian departments based on the quality of the data and to build the national ranking. Materials and methods: We analyzed the accumulated cases published between March 6 and September 1, 2020, by the Instituto Nacional de Salud. To achieve comparability, the analyses considered the day the first case was diagnosed as the first analysis date for each department. The fulfillment of Benford’s law was assessed with p-values in the log-likelihood ratio or chi-square tests. The analysis was completed with the lethality observed in each department and then the performance ranking was established. Results: Bogotá and Valle del Cauca had optimal public health surveillance performance all along. The data suggest that Antioquia, Nariño, and Tolima had good containment and adequate public health surveillance after the economic opening beginning on June 1, 2020. Conclusion: We obtained the ranking of the departments regarding the quality of public health surveillance data. The best five departments can be case studies to identify the elements associated with good performance.

Desempeño del sistema de vigilancia colombiano durante la pandemia de COVID-19: evaluación rápida de los primeros 50 días

Introducción. La pandemia de COVID es un desafío para la vigilancia en salud pública y una oportunidad para evaluar sus fortalezas y debilidades en aras de mejorar la respuesta.Objetivo. Evaluar el desempeño del sistema de vigilancia en salud pública colombiano durante los primeros 50 días de la pandemia de COVID-19 en el país.Materiales y métodos. Se analizaron los datos publicados entre el 6 de marzo y el 24 de abril de 2020 por el Instituto Nacional de Salud y la Organización Mundial de Salud (OMS). Se consideraron en la evaluación: i) la calidad de los datos según la ley de Benford y ii) la oportunidad de la información, medida como la diferencia en fechas entre los datos generados en el Instituto Nacional de Salud y los recogidos en el informe situacional de la OMS. La variabilidad en el cumplimiento de la ley de Benford se evaluó con los valores de p en las pruebas de razón del logaritmo de la verosimilitud, ji al cuadrado o exacta de Moreno.Resultados. Hasta el 24 de abril hubo 4.881 casos de COVID-19 en Colombia. En la mayoría de los primeros 50 días se cumplió la ley de Benford, excepto en los primeros días de la epidemia. La diferencia entre los informes del Instituto Nacional de Salud y la OMS ha dependido, en gran medida, de la diferencia en los horarios de cierre de la información.Conclusión. En general, el sistema de vigilancia en salud pública colombiano cumplió con la ley de Benford, lo cual sugiere que hubo calidad en los datos. En futuros estudios que comparen el desempeño de los departamentos y distritos se podrá mejorar el diagnóstico de la vigilancia en salud pública del país.

Data Quality of Chinese Surveillance of COVID-19: Objective Analysis Based on WHO's Situation Reports

Was there quality in the Chinese epidemiological surveillance system during the COVID-19 pandemic? Using data of World Health Organization's situation reports (until situation report 55), an objective analysis was realized to answer this important question. Fulfillment of Benford's law (first digit law) is a rapid tool to suggest good data quality. Results suggest that China had an acceptable quality in its epidemiological surveillance system. Furthermore, more detailed and complete analyses could complement the evaluation of the Chinese surveillance system.